A computational model of the semantics of tense and aspect
Computational Linguistics - Special issue on tense and aspect
Learning to resolve natural language ambiguities: a unified approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A Two-Level Knowledge Representation for Machine Translation: Lexical Semantics and Tense/Aspect
Proceedings of the First SIGLEX Workshop on Lexical Semantics and Knowledge Representation
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
A simple approach to building ensembles of Naive Bayesian classifiers for word sense disambiguation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Granularity effects in tense translation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Syntactic features for high precision word sense disambiguation
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Modeling consensus: classifier combination for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Word Sense Disambiguation: Algorithms and Applications (Text, Speech and Language Technology)
Word Sense Disambiguation: Algorithms and Applications (Text, Speech and Language Technology)
Ensemble methods for unsupervised WSD
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
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EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
Learning sentence-internal temporal relations
Journal of Artificial Intelligence Research
The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Tense tagging for verbs in cross-lingual context: a case study
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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Polysemy is a major characteristic of natural languages. Like words, syntactic forms can have several meanings. Understanding the correct meaning of a syntactic form is of great importance to many NLP applications. In this paper we address an important type of syntactic polysemy --- the multiple possible senses of tense syntactic forms. We make our discussion concrete by introducing the task of Tense Sense Disambiguation (TSD): given a concrete tense syntactic form present in a sentence, select its appropriate sense among a set of possible senses. Using English grammar textbooks, we compiled a syntactic sense dictionary comprising common tense syntactic forms and semantic senses for each. We annotated thousands of BNC sentences using the defined senses. We describe a supervised TSD algorithm trained on these annotations, which outperforms a strong baseline for the task.